A methodology for optimized Energy Storage sizing with stochastic resources

This paper proposes a systematic approach to size an optimized Energy Storage System (ESS) to reduce the cost of energy to a distribution system with a high penetration of wind generation. The optimized sizing depends on the marginal benefit of implementing a higher ESS size versus the cost of increasing the ESS size, and it can be maximized through arbitrage in jurisdictions that implement a Time-of-Use (TOU) energy pricing scheme. Monte Carlo Simulations are performed to determine the random distribution of cost benefits of implementing a pre-sized ESS, to which a least-squares error approach to surface fitting is used to determine the expected monetized benefit. The optimized size is then obtained by setting the derivative of the benefit minus the cost of implementation curves to zero, and solving for the sizes.